Genome-wide screening of DNA methylation in bovine blastocysts with different kinetics of development
Bibliographic record
Abstract
BACKGROUND: The timing of the first cell divisions may predict the developmental potential of an embryo, including its ability to establish pregnancy. Besides differences related to metabolism, stress, and survival, embryos with different speeds of development present distinct patterns of gene expression, mainly related to energy and lipid metabolism. As gene expression is regulated by epigenetic factors, and that includes DNA methylation patterns, in this study we compared the global DNA methylation profile of embryos with different kinetics of development in order to identify general pathways and regions that are most influenced by this phenotype. For this purpose, bovine embryos were in vitro produced using sexed semen (female), classified as fast (four or more cells) or slow (two cells) at 40 hpi and cultured until blastocyst stage, when they were analyzed. RESULTS: Genome-wide DNA methylation analysis identified 11,584 differently methylated regions (DMRs) (7976 hypermethylated regions in fast and 3608 hypermethylated regions in slow embryos). Fast embryos presented more regions classified as hypermethylated distributed throughout the genome, as in introns, exons, promoters, and repeat elements while in slow embryos, hypermethylated regions were more present in CpG islands. DMRs were clustered by means of biological processes, and the most affected pathways were related to cell survival/differentiation and energy/lipid metabolism. Transcripts profiles from DM genes connected with these pathways were also assessed, and the most part disclosed changes in relative quantitation. CONCLUSION: The kinetics of the first cleavages influences the DNA methylation and expression profiles of genes related to metabolism and differentiation pathways and may affect embryo viability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".